Noise Reduction Method For Images Research Articles

Hyperspectral low altitude UAV target tracking algorithm based on deep learning and improved KCF

This article presents a novel target tracking algorithm for hyperspectral low altitude UAV, combining deep learning with an improved Kernelized Correlation Filter (KCF). Initially, an image noise reduction method based on principal component analysis with Block-Matching 3D (BM3D), is employed to process redundant information. Subsequently, an image fusion method is utilized to merge the processed hyperspectral image and the high-resolution panchromatic band image to obtain a high spatial resolution image for target enhancement. Following this, YOLOv5 is used to detect the coordinate information of the UAV target in the current frame. Then, The KCF algorithm is used for target tracking in the current frame using kernel correlation filtering. Finally, the Discriminative Scale Spatial Tracker (DSST) is employed to determine the scale information to achieve a multi-scale tracking effect. The experimental results demonstrate that the algorithm presented in this paper surpasses CSK, HLT, and the conventional KCF algorithm in hyperspectral UAV datasets. On average, there is a significant increase in accuracy which is over 17% when using our algorithm.

A novel image noise reduction method for composite multistable stochastic resonance systems

In the field of digital signal processing, image denoising is an more and more significant research direction. For the traditional noise reduction theory, noise is considered to be harmful, and the image quality can be improved by analyzing noise characteristics and filtering noise. The appearance of stochastic resonance theory proves that noise can be used to enhance signal, which brings new inspiration to image processing. The classical bistable stochastic resonance model has the problems of high potential barrier and easy saturation, which is not conducive to the improvement of image denoising effect. In this paper, a novel type of stochastic resonance potential well model is quoted, which solves the above shortcomings of the bistable stochastic resonance model, and then combines it with the Gaussian model to propose a composite multistable stochastic resonance model. The dynamic principle of the model in signal detection is described, and the influence of system parameters on image noise reduction is analyzed. The whale optimization algorithm is used to optimize the model parameters, and an adaptive compound multistable stochastic resonance system is established to process pictures and measured radar images under different noise backgrounds. The simulation experiment and engineering application show that the model proposed in this paper solves the problem of high potential barrier and easy saturation of the bistable model, and has better image noise reduction ability compared with Wiener filter, median filter, classical bistable stochastic resonance system and novel type of stochastic resonance potential well system.

Impulsive Noise Suppression Methods Based on Time Adaptive Self-Organizing Map

Removal of noise and restoration of images has been one of the most interesting topics in the field of image processing in the past few years. Existing filter-based methods can remove image noise; however, they cannot preserve image quality and information such as lines and edges. In this article, various classifiers and spatial filters are combined to achieve desirable image restoration. Meanwhile, the time adaptive self-organizing map (TASOM) classifier is more emphasized in our feature extraction and dimensionality reduction approaches to preserve the details during the process, and restore the images from noise. The TASOM was compared with the self-organizing map (SOM) network, and a suitable noise reduction method for images was attempted. As a result, we achieved an optimum method to reduce impulsive noise. In addition, by using this neural network, better noise suppression was achieved. Experimental results show that the proposed method effectively removes impulse noise and maintains color information as well as image details.

Automatic de-noising of close-range hyperspectral images with a wavelength-specific shearlet-based image noise reduction method

Hyperspectral imaging (HSI) has become an essential tool for exploration of different spatially-resolved properties of materials in analytical chemistry. However, due to various technical factors such as detector sensitivity, choice of light source and experimental conditions, the recorded data contain noise. The presence of noise in the data limits the potential of different data processing tasks such as classification and can even make them ineffective. Therefore, reduction/removal of noise from the data is a useful step to improve the data modelling. In the present work, the potential of a wavelength-specific shearlet-based image noise reduction method was utilised for automatic de-noising of close-range HS images. The shearlet transform is a special type of composite wavelet transform that utilises the shearing properties of the images. The method first utilises the spectral correlation between wavelengths to distinguish between levels of noise present in different image planes of the data cube. Based on the level of noise present, the method adapts the use of the 2-D non-subsampled shearlet transform (NSST) coefficients obtained from each image plane to perform the spatial and spectral de-noising. Furthermore, the method was compared with two commonly used pixel-based spectral de-noising techniques, Savitzky-Golay (SAVGOL) smoothing and median filtering. The methods were compared using simulated data, with Gaussian and Gaussian and spike noise added, and real HSI data. As an application, the methods were tested to determine the efficacy of a visible-near infrared (VNIR) HSI camera to perform non-destructive automatic classification of six commercial tea products. De-noising with the shearlet-based method resulted in a visual improvement in the quality of the noisy image planes and the spectra of simulated and real HSI. The spectral correlation was highest with the shearlet-based method. The peak signal-to-noise ratio (PSNR) obtained using the shearlet-based method was higher than that for SAVGOL smoothing and median filtering. There was a clear improvement in the classification accuracy of the SVM models for both the simulated and real HSI data that had been de-noised using the shearlet-based method. The method presented is a promising technique for automatic de-noising of close-range HS images, especially when the amount of noise present is high and in consecutive wavelengths.

Effectiveness of image noise reduction method for detection of early ischemic changes of acute stroke in unenhanced CT

Effectiveness of image noise reduction method for detection of early ischemic changes of acute stroke in unenhanced CT

Image denoising using generalised Cauchy filter

In many image processing analysis, it is important to significantly reduce the noise level. This study aims at introducing an efficient method for this purpose based on generalised Cauchy (GC) distribution. Therefore, some characteristics of GC distribution is considered. In particular, the characteristic function of a GC distribution is derived by using the theory of positive definite densities and utilising the density of a GC random variable as the characteristic function of a convolution of two generalised non-symmetric Linnik variables. Further, GC distribution is considered as a filter and in the proposed method for image noise reduction the optimal parameters of GC filter is defined by using the particle swarm optimisation. The proposed method is applied to different types of noisy images and the obtained results are compared with four state-of-the-art denoising algorithms. Experimental results confirm that their method could significantly reduce the noise effect.

Image Noise Reduction Method Based on Compatibility with Adjacent Pixels

This paper proposes an efficient noise reduction method for gray and color images that are contaminated by salt-and-pepper noise. In the proposed method, the pixels that are more compatible with adjacent pixels are replaced with target (noisy) pixels. The algorithm is applied on noisy Lena and Mansion images that are contaminated by salt-and-pepper noise with 0.1 and 0.2 noise intensities. Although this method is developed for reducing noise from the images that are contaminated by salt-and-pepper noise, it can also reduce the noise from the images that are contaminated by other types of noises; yet it is more efficient for reducing salt-and-pepper noise. Both numerical and visual comparisons are demonstrated in the experimental simulations. The results show the proposed algorithm can successfully remove impulse noise from images that are contaminated by salt-and-pepper noise.

A new technique for noise reduction at coronary CT angiography with multi-phase data-averaging and non-rigid image registration.

To investigate the feasibility of a newly developed noise reduction technique at coronary CT angiography (CTA) that uses multi-phase data-averaging and non-rigid image registration. Sixty-five patients underwent coronary CTA with prospective ECG-triggering. The range of the phase window was set at 70-80% of the R-R interval. First, three sets of consecutive volume data at 70%, 75% and 80% of the R-R interval were prepared. Second, we applied non-rigid registration to align the 70% and 80% images to the 75% image. Finally, we performed weighted averaging of the three images and generated a de-noised image. The image noise and contrast-to-noise ratio (CNR) in the proximal coronary arteries between the conventional 75% and the de-noised images were compared. Two radiologists evaluated the image quality using a 5-point scale (1, poor; 5, excellent). On de-noised images, mean image noise was significantly lower than on conventional 75% images (18.3 HU ± 2.6 vs. 23.0 HU ± 3.3, P < 0.01) and the CNR was significantly higher (P < 0.01). The mean image quality score for conventional 75% and de-noised images was 3.9 and 4.4, respectively (P < 0.01). Our method reduces image noise and improves image quality at coronary CTA. • We introduce a new method for image noise reduction at cardiac CT. • Multiple data acquisitions of an object and their averaging yield lower noise. • Our method uses multi-phase images reconstructed from unused redundant imaging data. • It reduces image noise by averaging multi-phase images transformed by non-rigid registration. • This method achieves a 20% image noise reduction at cardiac CT.

Parameterized Image Filtering Using fuzzy Logic

The principal source of blur in digital images arise during image acquisition (digitization) or transmission. The performance of imaging sensors is affected by a variety of factors, such as the environmental conditions during image acquisition. Blurry images are the result of movement of the camera during shooting (not holding it still) or the camera not being capable of choosing a fast enough shutter speed to freeze the action under the light conditions. For instance, in acquiring images with a camera, light levels and sensor temperature are major factors affecting the amount of blur in the resulting image. Blur was implemented by first creating a PSF filter in MatLab that would approximate linear motion blur. This PSF was then convolved with the original image to produce the blurred image. Convolution is a mathematical process by which a signal, in this case the image, is acted on by a system, the filter, in order to find the resulting signal. The amount of blur added to the original image depended on two parameters of the PSF: length of blur (in pixels), and the angle of the blur. This thesis work is going to provide a new, faster, and more efficient noise reduction method for images corrupted with motion blur. This new filter has two separated steps or phases: the detection phase and the filtering phase. The detection phase uses fuzzy rules to determine whether a image is blurred or not. When blurry image is detected, Then we use fuzzy filtering technique focuses only on the on the real blurred pixels.

散斑干涉相位条纹图的频域滤波处理

In order to solve the problem of serious influence of much noise in the phase fringe patterns of digital speckle pattern interferometry on the result and accuracy of phase unwrapping,an effective method of stripe sine / cosine decomposition combined with frequency domain low-pass filtering is introduced in this paper,and the high precision filter of phase fringe patterns of digital speckle pattern interferometry is realized. Basic idea of this method is that phase fringe patterns is mapped into two patterns by the sine / cosine function before the phase image filter processing,and then is synthesized into phase fringe patterns through frequency domain filter. This decomposition frequency domain filtering method can retention phase jump information effectively when filtering. Experimental results show that compared with the traditional image noise reduction method,this method can filter out the speckle noise in images better on the basis of reserving "rush"image information,and it is simple and effective,which solves the problem of loss from 10% to 40% for phase fringe patterns gray level information when traditional filtering methods are used in stripe phase fringe patterns.

Multi-level denoising and enhancement method based on wavelet transform for mine monitoring

Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented. Firstly, we used directional median filter to effectively reduce impulse noise in the spatial domain, which is the main cause of noise in mine. Secondly, we used a Wiener filtration method to mainly reduce the Gaussian noise, and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain. This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits, and effectively reduce impulse noise and Gaussian noise in a coal mine, while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.

A new evaluation method for image noise reduction and usefulness of the spatially adaptive wavelet thresholding method for CT images

We have proposed a direct evaluation method concerning preservation of noise-free components for image noise reduction. This evaluation method is to graphically estimate how well a noise-reduction method will preserve noise-free image components by using the normal probability plot of the image pixel value difference between an original image and its noise-reduced image; this difference is equivalent to the "method noise" which was defined by Buades etal. Further, by comparing the linearity of a normal probability plot for two different noise reduction methods, one can graphically assess which method will be more able to preserve the noise-free component than the other. As an illustrative example of this evaluation method, we have evaluated the effectiveness of the spatially-adaptive BayesShrink noise-reduced method devised by Chang etal., when applied to chest phantom CT images. The evaluation results of our proposed method were consistent with the visual impressions for the CT images processed in this study. The results of this study also indicate that the spatially-adaptive BayesShrink algorithm devised by Chang etal. will work well on the chest phantom CT images, although the assumption for this method is often violated in CT images, and the assumption postulated for the spatially-adaptive BayesShrink method is expected to have sufficient robustness for CT images.

Dictionary learning and similarity regularization based image noise reduction

Redundant dictionary learning based image noise reduction methods explore the sparse prior of patches and have proved to lead to state-of-the-art results; however, they do not explore the non-local similarity of image patches. In this paper we exploit both the structural similarities and sparse prior of image patches and propose a new dictionary learning and similarity regularization based image noise reduction method. By formulating the image noise reduction as a multiple variables optimization problem, we alternately optimize the variables to obtain the denoised image. Some experiments are taken on comparing the performance of our proposed method with its counterparts on some benchmark natural images, and the superiorities of our proposed method to its counterparts can be observed in both the visual results and some numerical guidelines.

A Prospective Comparison of Standard-Dose CT Enterography and 50% Reduced-Dose CT Enterography With and Without Noise Reduction for Evaluating Crohn Disease

The purpose of this study was to prospectively compare standard-dose CT enterography (CTE) and 50% reduced-dose CTE, obtained with and without an image noise reduction method, in the evaluation of Crohn disease. Ninety-two patients (69 men and 23 women; mean age [± SD], 31.2 ± 9.5 years) with Crohn disease underwent CTE. Using a dual-source scanner equipped with a proprietary noise reduction method (iterative reconstruction in image space [IRIS]), three sets of CTE images were obtained: standard-dose filtered back projection (FBP) (i.e., weighted FBP), low-dose (i.e., 50% reduction) FBP, and low-dose IRIS CTE. Image noise was measured. Two independent radiologists evaluated subjective image quality (1 [worst] to 4 [best]) and findings of active Crohn disease in the terminal small-bowel segment, including mural hyperenhancement, thickening and stratification, comb sign, and increased perienteric fat attenuation (1 [definitely absent] to 5 [definitely present]). The mean (± SD) volume CT dose index (CTDI(vol)) was 7.0 ± 0.9 mGy and 3.5 ± 0.5 mGy for standard-dose and low-dose CTE examinations, respectively. The mean (± SD) image noise for standard-dose FBP, low-dose FBP, and low-dose IRIS CTE was 10.6 ± 1.7 HU, 13.9 ± 2.1 HU, and 9.7 ± 1.7 HU, respectively (p < 0.001 for all comparisons). Both assessors found that image quality was poorer with low-dose (mean grade (± SD), 2.3 ± 0.4-2.7 ± 0.5) than in standard-dose (3 ± 0) CTE (p < 0.01), and one found that image quality was poorer with low-dose IRIS (2.3 ± 0.4) than with low-dose FBP (2.7 ± 0.5) CTE (p < 0.01). Low-dose (with or without IRIS) and standard-dose CTE showed ≥ 85% agreement (one-sided 95% CI ≥ 77%) in interpretation of bowel findings. Low-dose CTE using 50% reduced-dose performed similarly to standard-dose CTE in identifying findings of enteric inflammation of Crohn disease. Although a noise reduction method markedly reduced image noise in half-dose examinations, its effect on image quality was not as great and was reader dependent.